In [31]:
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_white"
In [32]:
data = pd.read_csv("Colorado.csv", encoding='latin-1')
print(data.head())

   ID  Mountain Peak          Mountain Range  Elevation_ft fourteener  \
0   1   Mount Elbert           Sawatch Range         14440          Y   
1   2  Mount Massive           Sawatch Range         14428          Y   
2   3  Mount Harvard           Sawatch Range         14421          Y   
3   4    Blanca Peak  Sangre de Cristo Range         14351          Y   
4   5  La Plata Peak           Sawatch Range         14343          Y   

   Prominence_ft  Isolation_mi    Standard Route  Distance_mi  \
0           9093        670.00  Northeast Ridge          9.50   
1           1961          5.06      East Slopes         14.50   
2           2360         14.93     South Slopes         14.00   
3           5326        103.40  Northwest Ridge         17.00   
4           1836          6.28  Northwest Ridge          9.25   

   Elevation Gain_ft Difficulty  Traffic Low  Traffic High  
0               4700    Class 1        20000         25000  
1               4500    Class 2         7000         10000  
2               4600    Class 2         5000          7000  
3               6500    Class 2         1000          3000  
4               4500    Class 2         5000          7000
In [33]:
print(data.isnull().sum())

ID                   0
Mountain Peak        0
Mountain Range       0
Elevation_ft         0
fourteener           0
Prominence_ft        0
Isolation_mi         0
Standard Route       0
Distance_mi          0
Elevation Gain_ft    0
Difficulty           0
Traffic Low          0
Traffic High         0
dtype: int64
In [34]:
print(data.columns)

Index(['ID', 'Mountain Peak', 'Mountain Range', 'Elevation_ft', 'fourteener',
       'Prominence_ft', 'Isolation_mi', 'Standard Route', 'Distance_mi',
       'Elevation Gain_ft', 'Difficulty', 'Traffic Low', 'Traffic High'],
      dtype='object')

Correlation between Elevation_ft & Elevation Gain_ft¶

In [35]:
#Select the columns

column1  = data['Elevation_ft']
column2  = data['Elevation Gain_ft']
In [36]:
# Calculate the correlation coefficient

correlation_coefficient = column1.corr(column2)
In [37]:
print("Correlation Coefficient:", correlation_coefficient)

Correlation Coefficient: -0.07298694388781231

Box Plot Elevation_ft¶

In [38]:
fig = go.Figure()
fig.add_trace(go.Box(y=data['Elevation_ft'], 
                     name='Elevation_ft'))
fig.update_layout(title='Elevation_ft Box Plot', 
                  yaxis_title='Elevation_ft')
fig.show()

Difficlty Trend by Elevation¶

In [42]:
fig = go.Figure()
fig.add_trace(go.Bar(x=data['Elevation_ft'], 
                         y=data['Difficulty'], 
                          name='Difficulty by Elevation_ft'))
fig.update_layout(title='Difficulty Trend', xaxis_title='Elevation_ft', 
                  yaxis_title=' Difficulty')
fig.show()

Prominence_ft by Mountain Range¶

In [65]:
import matplotlib.pyplot as plt
data.reset_index().plot(x="Mountain Range", 
                        y="Prominence_ft", 
                        figsize=(15,12), kind="bar",
                       title = "Prominence_ft by Mountain Range")
plt.style.use('fivethirtyeight')
plt.show()

Ratings by Difficulty¶

In [51]:
ratings = data["Difficulty"].value_counts()
numbers = ratings.index
quantity = ratings.values

import plotly.express as px
figure = px.pie(data, 
             values=quantity, 
             names=numbers,hole = 0.5)
figure.show()

Analyzing 14ers Elevation Gain_ft¶

In [54]:
import seaborn as sns
plt.figure(figsize=(10, 8))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impressions From  Prominence_ft")
sns.distplot(data['Elevation Gain_ft'])
plt.show()

C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

Analyzing 14ers Distance_mi¶

In [56]:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Distance_mi")
sns.distplot(data['Distance_mi'])
plt.show()

C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

Analyzing 14ers Isolation_mi¶

In [57]:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Explore")
sns.distplot(data['Isolation_mi'])
plt.show()

C:\Users\yemiw\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

Sum of Impressions on 14ers From Various Sources¶

In [63]:
Prominence_ft   = data["Prominence_ft"].sum()
Distance_mi = data["Distance_mi"].sum()
Isolation_mi = data["Isolation_mi"].sum()

labels = ['Prominence_ft','Distance_mi','Isolation_mi']
values = [ Prominence_ft , Distance_mi, Isolation_mi]

fig = px.pie(data, values=values, names=labels, 
             title='Impressions on 14ers From Various Sources', hole=0.5)
fig.show()